Unlocking LLM Security: Automated Penetration Testing and Source Code Review Based on Vulnerability Analysis and NLP

Abstract

This paper introduces an innovative framework designed to bolster the security of large language models (LLMs) and source code through automated penetration testing and vulnerability analysis. By integrating natural language processing (NLP) and machine learning, the proposed system overcomes the shortcomings of conventional security tools, offering a unified solution for testing LLMs and reviewing source code across multiple programming languages. The architecture employs API-based testing to probe LLMs for vulnerabilities such as prompt injections and data leaks, while leveraging static and dynamic analysis to detect coding flaws like SQL injection and buffer overflows. Experimental evaluations confirm the system's efficacy in identifying security risks, with features including real-time monitoring and an intuitive interface enhancing its accessibility to developers of varying expertise. Despite its promising accuracy and efficiency, challenges such as false positives and reliance on dataset quality are noted, suggesting directions for future refinement. This work advances AIdriven cybersecurity by delivering a scalable, adaptable toolset for securing modern software systems and intelligent models, contributing significantly to the evolving landscape of digital security.